where \(P_{\mathcal {K}}\)  refers to the precision of the model when evaluated on known classes and \(P_{\mathcal {K} \cup \mathcal {U}}\)  is the precision when evaluated on known and unknown classes, measured at a recall level \(R\)  (0.8 in all experiments). Ideally, WI should be less as the precision must not drop when unknown objects are added to the test set. Besides WI, we also use Absolute Open-Set Error (A-OSE) [1]}
to report the number count of unknown objects that get wrongly classified as any of the known class.
Both WI and A-OSE implicitly measure how effective the model is in handling unknown objects.
